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<p>The rapid spread of artificial intelligence (AI) in education is reshaping learning environments, yet there is limited evidence on how students adopt and benefit from these tools across different contexts. This study addresses that gap by examining AI adoption and its learning outcomes among university students, with a focus on digital inequality and cross-cultural differences. We develop an integrated framework that combines the Unified Theory of Acceptance and Use of Technology (UTAUT) with “smart divide” perspectives, incorporating motivational factors, digital access, skills, and AI literacy. Using survey data from 800 students in Pakistan and Malaysia and applying structural equation modeling with multi-group analysis, the findings show that UTAUT-based motivational factors (β = 0.47, <i>p</i> &lt;.001) and smart divide conditions (β = 0.34, <i>p</i> &lt;.001) jointly predicted students’ behavioral intention; behavioral intention strongly predicted actual use (β = 0.61, <i>p</i> &lt;.001), which in turn significantly enhanced learning engagement (β = 0.58, <i>p</i> &lt;.001) and perceived learning gains (β = 0.55, <i>p</i> &lt;.001). Cross-cultural results indicate that infrastructural and skill-related factors matter more in Pakistan, while learning benefits remain consistent across contexts once AI is used. The study extends technology adoption theory to AI-enhanced education and provides empirical evidence for the role of digital inequality. It highlights the importance of institutional support, equitable access, and AI literacy to ensure that AI integration in higher education is inclusive and effective.</p>

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Bridging the smart divide: a cross-cultural study of AI-enhanced learning adoption in Pakistan and Malaysia

  • 期刊论文

  • 发表日期：

    2026年5月2日

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  • Shahbaz Aslam,
  • Feng Qin,
  • Joana Jaya,
  • Intikhab Ahmad,
  • Sobia Abid,
  • Babar Hussain,
  • Arshad Ali,
  • Muhammad Zahid Bilal

摘要

The rapid spread of artificial intelligence (AI) in education is reshaping learning environments, yet there is limited evidence on how students adopt and benefit from these tools across different contexts. This study addresses that gap by examining AI adoption and its learning outcomes among university students, with a focus on digital inequality and cross-cultural differences. We develop an integrated framework that combines the Unified Theory of Acceptance and Use of Technology (UTAUT) with “smart divide” perspectives, incorporating motivational factors, digital access, skills, and AI literacy. Using survey data from 800 students in Pakistan and Malaysia and applying structural equation modeling with multi-group analysis, the findings show that UTAUT-based motivational factors (β = 0.47, p <.001) and smart divide conditions (β = 0.34, p <.001) jointly predicted students’ behavioral intention; behavioral intention strongly predicted actual use (β = 0.61, p <.001), which in turn significantly enhanced learning engagement (β = 0.58, p <.001) and perceived learning gains (β = 0.55, p <.001). Cross-cultural results indicate that infrastructural and skill-related factors matter more in Pakistan, while learning benefits remain consistent across contexts once AI is used. The study extends technology adoption theory to AI-enhanced education and provides empirical evidence for the role of digital inequality. It highlights the importance of institutional support, equitable access, and AI literacy to ensure that AI integration in higher education is inclusive and effective.